From the course: Full-Stack Deep Learning with Python

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Identifying the best model

Identifying the best model - Python Tutorial

From the course: Full-Stack Deep Learning with Python

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Identifying the best model

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- [Instructor] Now that the hyper parameter optimization process is complete, the variable best result should hold the hyper parameters for the model with the best accuracy score on the validation data. So let's take a look at best result here. And this was the result where we chose the first option for l1 the second option for l2, and the learning rate was around 0.000285. Now, in order to know what the model parameters were for this best possible model, you can print out the results of the hyper opt of space eval function, pass in the search space, pass in the best result, and you'll get the hyper parameters for the best model. l1 has a value of 64, l2, a value of 256, and lr is 0.0002855. Now, let's go back to our ml flow experiment. Now, if you remember, we had added to this page, three different columns: the training accuracy, validation accuracy, and the test accuracy. If you scroll over to the right here, you'll see…

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